# Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images Source: [Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:45:52.748Z Authors: Yi Peng, Shihao Sun, Zheng Wang, Yining Pan, Ruirui Li Published: 2019-03-07T02:53:13.000Z ## Abstract Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications. Several factors such as shadows, weather disruption and camera shakes making this problem highly challenging, especially only using RGB images. In this paper, we propose the use of multi-modality data including NIR, RGB and DSM to increase robustness of segmentation in blurred or partially damaged VHR remote sensing images. By proposing a cascaded dense encoder-decoder network and the SELayer based fusion and assembling techniques, the proposed RobustDenseNet achieves steady performance when the image quality is decreasing, compared with the state-of-the-art semantic segmentation model. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()